BMC Bioinformatics

Table 1 Comparison against different methods on SCOP 1.53 superfamily benchmark

From: A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

Average ROC and ROC50 scores
Methods	ROC	ROC50	Source
SVM-Top-n-gram
n = 1	0.9069	0.696
n = 2	0.9230	0.713
n = 3	0.9073	0.653
SVM-Top-n-gram-combine	0.9329	0.763
SVM-Bprofile(Ph = 0.13)	0.9032	0.681	[28]
SVM-Ngram	0.7914	0.584	[32]
SVM-Pattern	0.8354	0.589	[32]
SVM-Motif	0.8136	0.616	[32]
SVM-Top-n-gram-combine-LSA	0.9390	0.767
SVM-Bprofile-LSA(Ph = 0.13)	0.9210	0.698	[28]
SVM-Ngram-LSA	0.8595	0.628	[32]
SVM-Pattern-LSA	0.8789	0.626	[32]
SVM-Motif-LSA	0.8592	0.628	[32]
PSI-BLAST	0.6754	0.330	[32]
SVM-Pairwise	0.8960	0.464	[11]
SVM-LA(β = 0.5)	0.9250	0.649	[11]
Profile(5,7.5)	0.9800	0.794	[10]
SW-PSSM(3.0,0.750,1.50)	0.9820	0.904	[10]

SVM-Ngram, SVM-Pattern, SVM-Motif, SVM-Bprofile and SVM-Top-n-gram refer to the methods based on the five building blocks: N-grams, patterns, motifs, binary profiles and Top-n-grams respectively. The methods with combine suffix refer to the methods combining Top-1-grams and Top-2-grams. The methods with LSA suffix refer to the corresponding methods after latent semantic analysis. Source is the sources of results.

Back to article page

ISSN: 1471-2105

Contact us

General enquiries: journalsubmissions@springernature.com